Bayesian estimators of the intraclass correlation coefficient in the one-way random effects model

Brent D Burch, Ian R. Harris

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In this paper Bayesian methods are used to estimate the intraclass correlation coefficient in the balanced one-way random effects model. An estimator associated with the likelihood function derived from a pivotal quantity along with estimators using reference priors are considered. In addition, an estimator based on a posterior median is examined. These estimators are compared to one another and to the REML (restricted maximum likelihood) estimator in terms of MSE (mean-squared error). A beta-type approximation to the pivotal likelihood is considered. This can be combined with a beta prior to produce closed-form expressions that approximate the posterior mean and mode. These approximations generally perform well as judged by Bayes risk. Of the estimators considered the authors recommend the one obtained from the pivotal approach. The authors indicate how the estimation procedures may be extended to the unbalanced one-way random effects model.

Original languageEnglish (US)
Pages (from-to)1247-1272
Number of pages26
JournalCommunications in Statistics - Theory and Methods
Volume28
Issue number6
StatePublished - 1999

Fingerprint

Intraclass Correlation Coefficient
Bayesian Estimator
Random Effects Model
Estimator
Maximum likelihood
Restricted Maximum Likelihood Estimator
Pivotal Quantity
Reference Prior
Bayes Risk
Posterior Mean
Bayesian Methods
Approximation
Likelihood Function
Mean Squared Error
Likelihood
Closed-form
Estimate

Keywords

  • Beta likelihood approximation
  • Pivotal quantity
  • Reference priors
  • REML estimator
  • Variance components

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Statistics and Probability

Cite this

Bayesian estimators of the intraclass correlation coefficient in the one-way random effects model. / Burch, Brent D; Harris, Ian R.

In: Communications in Statistics - Theory and Methods, Vol. 28, No. 6, 1999, p. 1247-1272.

Research output: Contribution to journalArticle

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